



'''simple docstring'''



import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase_  :							Union[str, Any]             =    logging.get_logger(__name__)

lowerCAmelCase_  :							Tuple             =    {'vocab_file': 'spm_char.model'}

lowerCAmelCase_  :							List[str]             =    {
    'vocab_file': {
        'microsoft/speecht5_asr': 'https://huggingface.co/microsoft/speecht5_asr/resolve/main/spm_char.model',
        'microsoft/speecht5_tts': 'https://huggingface.co/microsoft/speecht5_tts/resolve/main/spm_char.model',
        'microsoft/speecht5_vc': 'https://huggingface.co/microsoft/speecht5_vc/resolve/main/spm_char.model',
    }
}

lowerCAmelCase_  :							int             =    {
    'microsoft/speecht5_asr': 1024,
    'microsoft/speecht5_tts': 1024,
    'microsoft/speecht5_vc': 1024,
}
class 						lowerCamelCase_				(       snake_case_      ):
						_lowerCAmelCase  : Optional[Any]						  =					VOCAB_FILES_NAMES
						_lowerCAmelCase  : str						  =					PRETRAINED_VOCAB_FILES_MAP
						_lowerCAmelCase  : Any						  =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_lowerCAmelCase  : Tuple						  =					['input_ids', 'attention_mask']







						def __init__(	self :   str					,     lowerCAmelCase__ :   Tuple					,     lowerCAmelCase__ :   int="<s>"					,     lowerCAmelCase__ :   str="</s>"					,     lowerCAmelCase__ :   Optional[int]="<unk>"					,     lowerCAmelCase__ :   List[Any]="<pad>"					,     lowerCAmelCase__ :   Optional[Dict[str, Any]] = None					,     **lowerCAmelCase__ :   Any					,     ):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Dict         =      {} if sp_model_kwargs is None else sp_model_kwargs

													super().__init__(
													    bos_token=lowerCAmelCase__					,     eos_token=lowerCAmelCase__					,     unk_token=lowerCAmelCase__					,     pad_token=lowerCAmelCase__					,     sp_model_kwargs=self.sp_model_kwargs					,     **lowerCAmelCase__					,     )

													SCREAMING_SNAKE_CASE       :   Dict         =      vocab_file

													SCREAMING_SNAKE_CASE       :   Optional[int]         =      spm.SentencePieceProcessor(**self.sp_model_kwargs					)
													self.sp_model.Load(lowerCAmelCase__					)







						@property
						def        __lowercase     (	self :   Dict					):

													"""simple docstring"""
													return self.sp_model.get_piece_size()







						def        __lowercase     (	self :   Optional[Any]					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   int         =      {self.convert_ids_to_tokens(lowerCAmelCase__					): i for i in range(self.vocab_size					)}
													vocab.update(self.added_tokens_encoder					)
													return vocab







						def __getstate__(	self :   List[str]					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   List[str]         =      self.__dict__.copy()
													SCREAMING_SNAKE_CASE       :   Tuple         =      None
													return state







						def __setstate__(	self :   Dict					,     lowerCAmelCase__ :   Optional[int]					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      d

													# for backward compatibility
													if not hasattr(self					,     '''sp_model_kwargs'''					):
																				SCREAMING_SNAKE_CASE       :   Any         =      {}

													SCREAMING_SNAKE_CASE       :   List[str]         =      spm.SentencePieceProcessor(**self.sp_model_kwargs					)
													self.sp_model.Load(self.vocab_file					)







						def        __lowercase     (	self :   List[str]					,     lowerCAmelCase__ :   str					):

													"""simple docstring"""
													return self.sp_model.encode(lowerCAmelCase__					,     out_type=lowerCAmelCase__					)







						def        __lowercase     (	self :   Tuple					,     lowerCAmelCase__ :   List[str]					):

													"""simple docstring"""
													return self.sp_model.piece_to_id(lowerCAmelCase__					)







						def        __lowercase     (	self :   Tuple					,     lowerCAmelCase__ :   List[Any]					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Optional[int]         =      self.sp_model.IdToPiece(lowerCAmelCase__					)
													return token







						def        __lowercase     (	self :   Union[str, Any]					,     lowerCAmelCase__ :   Any					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   str         =      []
													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      ''''''
													for token in tokens:
																				# make sure that special tokens are not decoded using sentencepiece model
																				if token in self.all_special_tokens:
																											out_string += self.sp_model.decode(lowerCAmelCase__					) + token
																											SCREAMING_SNAKE_CASE       :   str         =      []
																				else:
																											current_sub_tokens.append(lowerCAmelCase__					)
													out_string += self.sp_model.decode(lowerCAmelCase__					)
													return out_string.strip()







						def        __lowercase     (	self :   Any					,     lowerCAmelCase__ :   Optional[Any]					,     lowerCAmelCase__ :   Optional[int]=None					):

													"""simple docstring"""
													if token_ids_a is None:
																				return token_ids_a + [self.eos_token_id]
													# We don't expect to process pairs, but leave the pair logic for API consistency
													return token_ids_a + token_ids_a + [self.eos_token_id]







						def        __lowercase     (	self :   Tuple					,     lowerCAmelCase__ :   List[int]					,     lowerCAmelCase__ :   Optional[List[int]] = None					,     lowerCAmelCase__ :   bool = False					):

													"""simple docstring"""
													if already_has_special_tokens:
																				return super().get_special_tokens_mask(
																				    token_ids_a=lowerCAmelCase__					,     token_ids_a=lowerCAmelCase__					,     already_has_special_tokens=lowerCAmelCase__					)

													SCREAMING_SNAKE_CASE       :   List[str]         =      [1]
													if token_ids_a is None:
																				return ([0] * len(lowerCAmelCase__					)) + suffix_ones
													return ([0] * len(lowerCAmelCase__					)) + ([0] * len(lowerCAmelCase__					)) + suffix_ones







						def        __lowercase     (	self :   Optional[int]					,     lowerCAmelCase__ :   str					,     lowerCAmelCase__ :   Optional[str] = None					):

													"""simple docstring"""
													if not os.path.isdir(lowerCAmelCase__					):
																				logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""					)
																				return
													SCREAMING_SNAKE_CASE       :   str         =      os.path.join(
													    lowerCAmelCase__					,     (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']					)

													if os.path.abspath(self.vocab_file					) != os.path.abspath(lowerCAmelCase__					) and os.path.isfile(self.vocab_file					):
																				copyfile(self.vocab_file					,     lowerCAmelCase__					)
													elif not os.path.isfile(self.vocab_file					):
																				with open(lowerCAmelCase__					,     '''wb'''					) as fi:
																											SCREAMING_SNAKE_CASE       :   Dict         =      self.sp_model.serialized_model_proto()
																											fi.write(lowerCAmelCase__					)

													return (out_vocab_file,)









'''simple docstring'''



from __future__ import annotations
def        UpperCAmelCase (    A	:					list[int]       ,		A	:					int					):
							if len(A					) < k or k < 0:
														raise ValueError('''Invalid Input'''					)
							SCREAMING_SNAKE_CASE       :   Dict         =      sum(array[:k]					)
							for i in range(len(A					) - k					):
														SCREAMING_SNAKE_CASE       :   Optional[Any]         =      current_sum - array[i] + array[i + k]
														SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      max(A       ,		A					)
							return max_sum


if __name__ == "__main__":
							from doctest import testmod
							from random import randint

							testmod()
							lowerCAmelCase_  :							Optional[int]             =    [randint(-1000, 1000) for i in range(100)]
							lowerCAmelCase_  :							Optional[int]             =    randint(0, 110)
							print(f'The maximum sum of {k} consecutive elements is {max_sum_in_array(array,k)}')




